Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Pulmonary Tuberculosis V01:28

Pulmonary Tuberculosis V

655
Medical management of tuberculosis (TB) patients involves a comprehensive approach that includes diagnosis, treatment, and monitoring. The specific strategies can vary depending on the type of tuberculosis (latent or active), the patient's overall health status, and other considerations.
Latent tuberculosis infection occurs when TB bacteria are present in a person's body, but are not causing illness or symptoms. It is not contagious, and preventive treatment is crucial to avoid the...
655
Pulmonary Tuberculosis IV01:26

Pulmonary Tuberculosis IV

556
Tuberculosis, more commonly referred to as TB, is an infectious disease stemming from Mycobacterium tuberculosis. While it primarily impacts the lungs, TB can also affect other body areas. Given its severity and global impact, timely and accurate diagnosis is crucial for controlling its spread and improving patient outcomes.
Several diagnostic approaches are used to detect TB. The conventional method is the Tuberculin Skin Test (TST), also known as the Mantoux test. However, this method has...
556

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

The functionality of the cysteinyl leukotriene receptor 1 (CysLTR1) in the lung by metabolomics analysis of bronchoalveolar lavage fluid.

Metabolomics : Official journal of the Metabolomic Society·2026
Same author

Dysregulated Metabolism in People Living With HIV in the Modern ART-Era: A Systematic Review of Targeted Metabolomics Studies.

Reviews in medical virology·2026
Same author

Integration of functional immunomonitoring assays with PET/CT scans in TB patients identifies on-treatment biomarkers.

bioRxiv : the preprint server for biology·2026
Same author

Risk of progression to pulmonary tuberculosis among household contacts with chest radiographic abnormalities in South Africa.

medRxiv : the preprint server for health sciences·2026
Same author

Editorial: Investigating lung biology, metabolism, and host susceptibility in mycobacterial infections.

Frontiers in cellular and infection microbiology·2026
Same author

Metabolomics of HIV in the Modern cART Era.

Advances in experimental medicine and biology·2026

Related Experiment Video

Updated: Feb 18, 2026

Sample Preparation of Mycobacterium tuberculosis Extracts for Nuclear Magnetic Resonance Metabolomic Studies
07:56

Sample Preparation of Mycobacterium tuberculosis Extracts for Nuclear Magnetic Resonance Metabolomic Studies

Published on: September 3, 2012

16.0K

Predicting tuberculosis treatment outcome using metabolomics.

Laneke Luies1, Mari van Reenen1, Katharina Ronacher2,3

  • 1School for Physical & Chemical Sciences, Human Metabolomics, North-West University (Potchefstroom Campus), Private Bag x6001, Box 269, Potchefstroom 2531, South Africa.

Biomarkers in Medicine
|November 28, 2017
PubMed
Summary
This summary is machine-generated.

Metabolomics analysis of urine samples can predict tuberculosis treatment failure at diagnosis. This finding aids in developing new drugs and personalizing patient care by identifying gut microbiota imbalances.

Keywords:
M. tuberculosismetabolomicspredicting treatment outcometreatment failuretuberculosis

More Related Videos

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
11:02

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics

Published on: November 29, 2024

1.3K
Validated LC-MS/MS Panel for Quantifying 11 Drug-Resistant TB Medications in Small Hair Samples
08:54

Validated LC-MS/MS Panel for Quantifying 11 Drug-Resistant TB Medications in Small Hair Samples

Published on: May 19, 2020

8.3K

Related Experiment Videos

Last Updated: Feb 18, 2026

Sample Preparation of Mycobacterium tuberculosis Extracts for Nuclear Magnetic Resonance Metabolomic Studies
07:56

Sample Preparation of Mycobacterium tuberculosis Extracts for Nuclear Magnetic Resonance Metabolomic Studies

Published on: September 3, 2012

16.0K
Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
11:02

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics

Published on: November 29, 2024

1.3K
Validated LC-MS/MS Panel for Quantifying 11 Drug-Resistant TB Medications in Small Hair Samples
08:54

Validated LC-MS/MS Panel for Quantifying 11 Drug-Resistant TB Medications in Small Hair Samples

Published on: May 19, 2020

8.3K

Area of Science:

  • Clinical chemistry
  • Microbiome research
  • Medical diagnostics

Background:

  • Predicting tuberculosis (TB) treatment outcomes is crucial for effective patient management and the development of novel therapeutics.
  • Current diagnostic methods may not fully capture the factors influencing treatment success or failure.

Purpose of the Study:

  • To investigate the potential of metabolomics for predicting TB treatment outcomes at the time of diagnosis.
  • To identify specific biomarkers associated with treatment failure.

Main Methods:

  • Urine samples were collected from TB-positive patients with known successful and unsuccessful treatment outcomes.
  • Metabolite profiling was performed, and identified metabolites were used in a forward logistic regression model.
  • Model performance was evaluated using receiver operating characteristic (ROC) analysis and cross-validation.

Main Results:

  • The logistic regression model achieved a high area under the curve (AUC) of 0.94 (95% CI: 0.84-1) in predicting treatment outcomes.
  • Cross-validation confirmed the model's robustness, with an AUC of 0.89 (95% CI: 0.7-1).
  • Two key metabolite predictors were identified, linked to gut microbiota imbalance.

Conclusions:

  • Metabolomics analysis demonstrates significant potential for predicting TB treatment failure early in the disease course.
  • These findings support the utility of metabolomics in clinical trial design for new drug development.
  • Early prediction enables personalized patient care strategies and timely intervention.